AI Code Is Cheap. Its Cleanup Isn't. Run a 90-Day Churn Gate

The dashboard looked great. Pull requests per week had doubled. The platform lead who shared it wasn't celebrating, though — he opened a second chart first. Files re-touched within two weeks of merge were climbing just as fast.

I keep seeing that pattern in adoption reviews. Teams measure what the AI produces: lines, PRs, test coverage. Almost nobody measures what the team pays afterward. That's the gap this workflow closes.

Here's a 90-day pilot design — a fixed token budget, a churn measurement script, and hard go/stay/stop gates. No invented ROI. Just a way to find out whether AI-generated code in your repo is an asset or a liability.

The cheap-write, expensive-own problem

Writing code is the cheapest part of software. AI made it nearly free. Reviewing, debugging, onboarding, and deleting code did not get cheaper. A 400-line generated diff costs the same review time, and every one of those lines becomes your debt forever.

So "what does this cost per month?" is the wrong question. The right one is: "what does it cost to integrate and maintain this code for the next 90 days?"

You need three things to answer it:

1. A marker that identifies AI-authored commits
2. A fixed budget so usage is an experiment, not a drift
3. A churn ratio that turns maintenance pain into a number

Why a free tier is a measurement tool, not just a discount

MonkeyCode is an open-source AI coding agent that gives teams free model access — 10 million tokens — and a free server option. Disclosure: This article was prepared as part of MonkeyCode's product outreach.

I'm not telling you to adopt it because it's free. I'm telling you to use its free tier the way you'd use any fixed-cost pilot environment: as a controlled budget. When the budget is visible, every generated line becomes a decision. Is this token spend worth the future maintenance? That's governance without a committee.

The product is the vehicle. The experiment design is the actual tool. And the script below works no matter which agent you use.

Step 1: Mark your AI commits

You can't measure what you can't identify. Make every AI-generated change carry a marker in the commit message:

git commit -m "[ai] add validation helper for webhook payloads"

That one habit is the foundation of everything that follows. If your team already routes AI work through a specific bot identity, you can match on author instead. Pick one marker and make it a PR checklist item for two weeks until it sticks.

Step 2: Measure churn with this script

Save this as ai_churn.sh and run it from your repository root. It counts AI-added lines, then counts how many lines in those same files were changed by later commits:

#!/usr/bin/env bash
# ai_churn.sh — rough rework signal for AI-authored commits
# Needs: git, awk. Set AI_MARKER to your team's convention.
set -euo pipefail

AI_MARKER="${AI_MARKER:-[ai]}"
LOOKBACK_DAYS="${LOOKBACK_DAYS:-90}"
FILTER="${1:-}"   # optional path prefix, e.g. src/ or tests/

mapfile -t commits < <(git log --since="$LOOKBACK_DAYS days ago" --grep="$AI_MARKER" --format=%H)

total_added=0
total_churn=0

for c in "${commits[@]}"; do
  files=$(git show "$c" --name-only --pretty=format: | grep -v '^$')
  added=$(git show "$c" --numstat --pretty=format: | awk '{a+=$1} END {print a+0}')
  churn=0

  for f in $files; do
    [[ -n "$FILTER" && "$f" != "$FILTER"* ]] && continue
    n=$(git log "$c..HEAD" --numstat --pretty=format: -- "$f" \
        | awk '{a+=$1; d+=$2} END {print a+d+0}')
    churn=$((churn + n))
  done

  total_added=$((total_added + added))
  total_churn=$((total_churn + churn))
done

echo "AI commits: ${#commits[@]}"
echo "AI lines added: $total_added"
echo "Lines changed in those files after merge: $total_churn"

if [ "$total_added" -gt 0 ]; then
  awk -v a="$total_churn" -v b="$total_added" 'BEGIN {printf "Churn ratio: %.2f\n", a/b}'
fi

Run it per category instead of once:

./ai_churn.sh src/
./ai_churn.sh tests/
./ai_churn.sh docs/

The churn ratio is a rough signal, not a verdict. It ignores files that were renamed or deleted, and it counts all later modifications regardless of who made them. That's fine for a pilot. You want direction, not precision.

Step 3: Turn lines into a budget

Now translate churn into something a finance person will believe:

rework_hours = churn_lines / lines_touched_per_hour
rework_cost  = rework_hours × loaded_burden_rate

Worked example, with your numbers replacing mine. Say the pilot produces 12,000 AI-added lines, and the churn ratio lands at 0.6. That's 7,200 lines touched again later. At a rough 150 lines per hour of touch-up work, that's about 48 developer-hours. At a $100/h loaded rate, $4,800 of rework — before you count reviewing the original generations.

Was the velocity worth $4,800? Maybe. Now it's a decision instead of a surprise.

Step 4: Apply gates per category

The magic is that gates live at the category level, not the team level. A model can be excellent at generating test fixtures and terrible at auth middleware. You won't know which until you split the numbers.

Gate	Churn ratio	What you do	Owner	Review date
Expand	< 0.30	Allow broader use in that category; add more agent permissions	Platform lead	Day 90
Restrict	0.30 – 0.70	One extra human reviewer; limit to non-critical paths	Engineering manager	Day 90
Stop	> 0.70	Rewrite manually; keep the budget for one-off spikes	Team lead	Day 45 mid-check

A scorecard like this is a conversation tool, not objective truth. The churn ratio can't tell you why a file was touched again. Maybe the AI wrote bad code. Maybe requirements changed. Treat the gate as a forcing function for a human conversation, not as a robot judge.

The important part is setting the thresholds before you see the data. Otherwise Day 90 becomes a debate about which number feels right.

What the free server is for

There's a second measurement hazard: environment drift. If the agent runs on a shared cloud seat for the first month and a local machine for the second, your churn numbers mix two different setups. MonkeyCode's free server option gives you a fixed place to run the pilot so the environment stays constant — useful for reproducible regeneration and for letting the review team poke at the same sandbox.

Again, that's a convenience, not the point. You could run this whole experiment against any agent that lets you tag commits. The budget discipline and the churn gate do the heavy lifting.

Read this before you run it

Who should not use this workflow:

• Teams that won't tag AI commits. Without the marker, the script measures nothing. Spend two weeks building the habit first.
• Teams in the middle of a big refactor. Churn will spike for reasons unrelated to AI quality, and the gates will yell false alarms.
• Single-developer repos. A sample of five commits gives you a churn ratio with no statistical spine.

One more honesty check: low churn doesn't prove the code is good. It might mean nobody dares to touch it. Pair this workflow with code review outcomes, not just git archaeology.

The question that matters

Teams are asking what happens to technical debt when AI makes code cheap. This is an answer you can run locally in an afternoon.

What threshold would change your pilot decision — 0.4? 0.6? Set the number before you spend the tokens. Otherwise Day 90 is just another opinion meeting. If you want a budgeted sandbox to run this in, MonkeyCode's free tier is one way to get it. The script doesn't care which agent you use, and neither should your decision.